Latest Posts

• Universal Manipulation Interface (UMI)

  Universal Manipulation Interface: UMI is an innovative framework designed to bridge the gap between human demonstration and robotic execution, enabling robots to learn complex manipulation tasks directly from human actions performed in natural settings. This approach addresses the limitations of traditional robot teaching methods, which often rely on controlled environments and expensive equipment. 

• Human-Robot Interaction (HRI)

  Human-Robot Interaction (HRI) is fundamentally different from Human-Computer Interaction (HCI). For decades, HCI has shaped the way we engage with digital systems—through keyboards, touchscreens, and increasingly, voice assistants. But as robots move from factories into homes, hospitals, and workplaces, a new challenge emerged. How to design interactions for machines that exist in the same physical space as us?

• Computer Vision

  Computer Vision: Human beings have survived by relying on rapid visual cues—detecting subtle movements in tall grass, discerning edible plants from poisonous ones, and identifying a friend from foe in split seconds. Sight was the original survival mechanism granting us the power to parse our environment swiftly and accurately. Today, machines can approximate that life-preserving instinct through computer vision.

• Intersection of Edge AI and Embodied AI

  Edge AI is the ability to run artificial intelligence algorithms directly on local devices—smartphones, sensors, robots—without constantly relying on cloud computing. Instead of sending data back and forth to a remote server, the device processes it on the spot. That means real-time decisions, lower latency, improved privacy, and independence from unreliable internet connections.

• Sensor Fusion

  Sensor Fusion: Embodied AI agents (robots, autonomous vehicles, etc.) are equipped with multiple sensors (e.g. cameras, LiDAR, radar, ultrasonic, IMU, GPS) to perceive their environment. Sensor fusion is the process of combining data from these sensors to produce a more accurate or robust understanding than any single sensor could provide.

• Markov Decision Processes

  Markov Decision Processes: PART I - What Is an MDP? A Markov Decision Process is a mathematical framework that helps make good decisions when outcomes aren’t 100% certain. While it sounds complicated, the main idea is straightforward:

• Adversarial Attacks

  What Are Adversarial Attacks? Over the past few years, researchers have demonstrated various ways to fool state-of-the-art systems. In one high-profile study, carefully crafted stickers on traffic signs confused self-driving cars. In another, hackers manipulated the LED lights on a robot vacuum, tricking its camera-based obstacle detector. These are few real world examples for adversarial attacks.

• AI Agents

  AI Agents: When we think about artificial intelligence, we often picture algorithms crunching data, generating text, or analyzing images. But what happens when AI needs to interact with the world—whether in a video game, a financial system, or even a physical robot? Here comes AI agents.

• A Brief History of Embodied AI

  A Brief History of Embodied AI Today, many people associate Artificial Intelligence with chatbots and algorithms analyzing vast data sets. But there’s another side to AI that’s all about real-world interaction: Embodied AI. It’s the branch of AI that puts machines (or agents) into physical environments—whether in actual hardware or simulations—so they can perceive, act, and learn more like living beings. Below is a concise tour of how embodied AI evolved from early robotic explorations to the dynamic field we see today.

• Glossary Top 50

  Embodied AI is an area of artificial intelligence focused on agents that interact with the world through a physical (or simulated) body. Embodied AI goes beyond purely abstract computational tasks by integrating perception (sight, hearing, touch, etc.), action (motor control), and decision-making to learn from and adapt to changing environments.